Social insect systems: ants, termites, wasps, bees, cockroaches, locusts…
Animals with social behaviors: penguins, birds, fish, sheep...
Artificial self-organizing systems
a Swarm consists of (i) a large number of (ii) homogenous (iii) autonomous (iv) relatively incapable or inefficient robots with (v) local sensing and communication capabilities.
In other words, Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment.
Search & Rescue
Surveillance & Monitoring
Medical Service using small-size robots
Military . . .
A larger range of task domain
Improved system performance
Lower economic cost
Swarm Robots are classified to different types according to the following criteria:
Pair 2 Robots
Limited Group multiple Robots
Infinite Group n>>1 Robots
Infinite communicative Robots
Every robot can communicate with all of the other robots, but it is not able to send a message to a particular robot
Every robot can communicate with any arbitrary robot by name or address
Robots are linked in a tree & may only communicate through this hierarchy
Robots are linked in a general graph
What is Reconfigurability?
Reconfigurability is the rate at which the swarm can spatially reorganize itself.
Reconfigurability is equivalent to the rate at which members can move with respect to one another .
Ability to operate under a wide range of group sizes.
In other words, coordination mechanisms are rather independent of the number of individuals in the group.
Swarm Robots can continue to operate despite large disturbances because of:
Simplicity of the individuals
Treats the robot as the fundamental unit of the model.
Describes the robot’s interactions with other robots & the environment.
Directly describes the collective behavior of the robotic swarm.
Computationally efficient because of fewer variables
- Technical challenges to scalability
- Performing physical tasks in the real world
Obstacle Avoidance & Beacon Navigation
The communication and robot location system iRobot ISIS allow each robot to commute with its neighbors and determined their range, bearing, and orientation
These low-level behaviors do not interact with other robots at all. They provide low-level motion control and obstacle avoidance for an individual robot.
EX: move or stop a robot
Pair behaviors also direct the actions of a single active robot, but they use the position and current state of only one neighbor that is the reference robot.
EX: Orient to another robot, fellow a robot
These behaviors form the bulk of the behavior library. They are responsible for guiding the actions of a single active robot based on the positions and current state of all of its neighbors. The entire set of neighbors are the reference robots.
EX: Follow a leader, clustering, grouping.
In Chain Formation problem, the aim is to move the robots so that they form a chain pattern.
The follow The Leader behavior dynamically constructs an ordered line of robots. This line is suitable for leading a group of robots into an area. Another behavior is required to control the leader.
+ The goal of the clustering behavior is to move the swarm to a centralized location in as small an area as possible. + The clusterIntoGroups behavior implements a primitive form of division of labor. It operates in two `steps; first, each robot selects a group to join, then the behavior moves robots in the same groups together, while moving entire groups away from each other.
“ A Taxonomy for Swarm Robots”, G. Dudek! M. Jenkinj E. Milios! and D. Wilkest
“ A Review of Studies in Swarm Robotics”, L. Bayindir, E. Sahin.
“ A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems”, K. Lerman, A. Martinoli, A. Galstyan.
- “Stupid Robot Tricks: A Behavior-Based Distributed Algorithm Library for Programming Swarms of Robots”, J. McLurkin
- “Swarm Robotics: From sources of inspiration to domains of application”, E. Sahin.